def setup(obj):
torch.set_num_threads(1)
obj.path = torch.rand(obj.size, dtype=torch.float, requires_grad=True)
shape = obj.size[-3], signatory.logsignature_channels(obj.size[-1], obj.depth)
obj.grad = torch.rand(shape)
obj.logsignature = signatory.LogSignature(obj.depth)(obj.path)
def __init__(self, input_dim, order, N):
super(Net, self).__init__()
self.N = N
self.order = order
self.fc1 = nn.Linear(input_dim, 200)
self.fc2 = nn.Linear(200, 200)
self.fc3 = nn.Linear(200, N * 10)
self.fc4 = nn.Linear(10, 1)
self.logsig1 = signatory.LogSignature(depth=order)
def setup(obj):
obj.path = torch.rand(obj.size,
dtype=torch.float,
requires_grad=True,
device='cuda')
shape = obj.size[-3], signatory.logsignature_channels(
obj.size[-1], obj.depth)
obj.grad = torch.rand(shape, device='cuda')
obj.logsignature = signatory.LogSignature(obj.depth)(obj.path)
def __init__(self, latent_dim, hidden_dim, output_dim, condition_dim,
order, N, d):
super().__init__(latent_dim, hidden_dim, output_dim, condition_dim)
self.order = order
self.N = N
self.d = d
self.fc1 = nn.Linear(output_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, N * 10)
self.fc4 = nn.Linear(10, d)
self.logsig1 = signatory.LogSignature(depth=order)
def __init__(self, input_dim, order, N, d):
super(Net_time, self).__init__()
self.level = np.array(
[len(w) for w in signatory.lyndon_words(d + 1, order)])
self.d = d
self.N = N
self.order = order
self.fc1 = nn.Linear(input_dim, 200)
self.fc2 = nn.Linear(200, 200)
self.fc3 = nn.Linear(200, N * 10)
self.fc4 = nn.Linear(10, self.d)
self.logsig1 = signatory.LogSignature(depth=order)
def setup(obj):
torch.set_num_threads(1)
obj.path = torch.rand(obj.size, dtype=torch.float)
obj.logsignature_instance = signatory.LogSignature(obj.depth)
obj.logsignature_instance.prepare(obj.size[-1])
def setup(obj):
obj.path = torch.rand(obj.size, dtype=torch.float, device='cuda')
obj.logsignature_instance = signatory.LogSignature(obj.depth)
obj.logsignature_instance.prepare(obj.size[-1])